September 27, 2021

Research Article and Research Tool: “ScienceWISE: Topic Modeling over Scientific Literature Networks”

The following article (preprint) was recently shared by the authors on arXiv.

Direct to ScienceWISE website.

Title

ScienceWISE: Topic Modeling over Scientific Literature Networks

Authors

A. Magalich, V. Gemmetto, D. Garlaschelli, A. Boyarsky
University of Leiden, The Netherlands

A. Martini, A. Cardillo, A. Constantin, O. Ruchayskiy, P. De Los Rios, K. Aberer
École Polytechnique Fédérale de Lausanne, Switzerland

A. Lutov, M. Khayati, P. Cudré-Mauroux
University of Fribourg, Switzerland

V. Palchykov
University of Leiden, The Netherlands and Institute for Condensed Matter Physics, Liviv, Ukraine

Source

via arXiv

Abstract

We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept-based browsing.

However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double-edged sword: a more fine-grained characterization of articles is possible, but at the cost of introducing more spurious relations among them. In this context, the challenge of continuously recommending relevant articles to users lies in tackling a network partitioning problem, where nodes represent articles and co-occurring concepts create edges between them.

In this paper, we discuss the three research directions we have taken for solving this issue: i) the identification of generic concepts to reinforce inter-article similarities; ii) the adoption of a bipartite network representation to improve scalability; iii) the design of a clustering algorithm to identify concepts for cross-disciplinary articles and obtain fine-grained topics for all articles.

Direct to Full Text Article (Preprint)
6 pages; PDF.

Direct to ScienceWise

2016-12-23_09-53-33

About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

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